I’m a Member of Technical Staff at Microsoft AI working on post-training, with a focus on coding, game-playing agents, and game development.
Previously, I was a Senior Research Scientist at Netflix, where I worked on AI-powered interactive experiences and research problems in generative models and games. Before that, I received a PhD at MIT, advised by Justin Solomon, and a B.S. in Computer Science and Mathematics and an M.S. in Mathematics, both at Stanford University.
Publications
Steering Readerly Dynamics: Trajectory-Guided Story Generation with Large Language Models
Preprint
Video4Spatial: Towards Visuospatial Intelligence with Context-Guided Video Generation
Conference on Computer Vision and Pattern Recognition (CVPR 2026) Findings, Denver, Colorado
Correctness-Guaranteed Code Generation via Constrained Decoding
Oral Presentation
Conference on Language Modeling (COLM 2025), Montreal, Canada
Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise
Oral Presentation
Conference on Computer Vision and Pattern Recognition (CVPR 2025), Nashville, TN
Infinite-Resolution Integral Noise Warping for Diffusion Models
Conference on Learning Representations (ICLR 2025), Singapore
Debiased Distribution Compression
International Conference on Machine Learning (ICML 2024), Vienna
Self-Consistent Velocity Matching of Probability Flows
Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA
Sampling with Mollified Interaction Energy Descent
Conference on Learning Representations (ICLR 2023), Kigali
Learning Proximal Operators to Discover Multiple Optima
Conference on Learning Representations (ICLR 2023), Kigali
Wasserstein Iterative Networks for Barycenter Estimation
Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark
Conference on Neural Information Processing Systems (NeurIPS 2021), online
Large-Scale Wasserstein Gradient Flows
Conference on Neural Information Processing Systems (NeurIPS 2021), online
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
Conference on Learning Representations (ICLR 2021), online
Continuous Regularized Wasserstein Barycenters
Conference on Neural Information Processing Systems (NeurIPS 2020), online
Supervised Fitting of Geometric Primitives to 3D Point Clouds
Oral Presentation
Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA
Branching Rules of Classical Lie Groups in Two Ways
Undergraduate honors thesis. Stanford University, 2018
